Impaired behavioural and molecular adaptations to dopamine denervation and repeated L‐DOPA treatment in Nur77‐knockout mice
Bibliographic record
Abstract
We have previously shown that dopamine (DA) denervation and repeated L-DOPA treatment modulate the pattern of Nur77 mRNA expression in the striatum. However, the exact role of this nuclear receptor in L-DOPA-induced molecular and behavioural adaptations observed in animal models of Parkinson's disease is still unknown. In the present study, we investigated the effects of Nur77 gene deletion on the development of behavioural sensitization and on changes in the regulation of neuropeptides and DA D(3) receptor expression following DA denervation and repeated L-DOPA treatment in Nur77+/+ and Nur77-/- hemiparkinsonian mice. One week postsurgery, hemiparkinsonian mice were treated with L-DOPA (10 mg/kg) plus benserazide (3 mg/kg) once a day for 7 days. Despite similar extents of nigrostriatal denervation, L-DOPA-induced rotational response was exacerbated in Nur77-/- mice compared to Nur77+/+ ones. However, the rate of increase of the rotational behaviour after repeated L-DOPA injections was similar in the two mouse strains. Lesioning the nigrostriatal pathway increased enkephalin (ENK) and neurotensin (NT) mRNA levels in both mouse strains. However, the up-regulation of these neuropeptides was significantly reduced in Nur77-/- mice. There was no difference in the modulation of D3 receptor density and dynorphin (DYN) mRNA expression between the two mouse strains. The present results suggest that Nur77 is involved in setting the threshold level for L-DOPA-induced rotational behaviour, rather than controlling the development of behavioural sensitization. This specific behavioural change is associated with a selective regulation of neuropeptide expression specifically in the indirect striatal output pathway.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".